Estimation of Tensile Strength of PLA Based Material Produced by Additive Manufacturing Using Machine Learning Algorithms

Yıl 2021, , 15 - 25, 31.12.2021

Koray Özsoy , Hamdi Sayın

https://doi.org/10.29130/dubited.1012892

Öz

Additive manufacturing and artificial intelligence techniques, which are important components of Industry 4.0, are frequently used in many areas today. Additive manufacturing methods are divided into seven classes within themselves. The fused deposition method is one of the most preferred methods of additive manufacturing. The part is produced by the layer-by-layer combination of the filament material used on the fused deposition method (FDM) manufacturing table. In the study, tensile samples were produced by using different processing parameters in the FDM method. Tensile samples were tested according to ASTM standards, and a data set was created with the values of tensile strength. The tensile strength values of the material produced using the data set on temperature, velocity, layer properties collected during the material production process were estimated using three different machine learning models. The results showed that using machine learning algorithms, the tensile strength can be predicted with an accuracy of 98,3% by the Partial Least Squares algorithm.

Anahtar Kelimeler

Additive manufacturing, Machine learning, Fused deposition modelling

Eklemeli İmalat ile Üretilen PLA Esaslı Malzemenin Çekme Dayanımının Makine Öğrenmesi Algoritmaları Kullanarak Tahmini

Öz

Endüstri 4.0'ın önemli bileşenlerinden olan eklemeli imalat ve yapay zekâ tekniklikleri günümüzde birçok alanda sıklıkla kullanılmaktadır. Eklemeli imalat yöntemleri kendi içerisinde yedi sınıfa ayrılmaktadır. Eriyik yığma modelleme eklemeli imalat yönteminin sıklıkla tercih edilen yöntemlerinden birisidir. Eriyik yığma modelleme imalat tablası üzerinde kullanılan filament malzemenin katman katman birleşimi ile parça üretimi gerçekleştirilir. Çalışmada eriyik yığma modelleme yönteminde farklı işleme parametreleri kullanılarak çekme numuneleri üretilmiştir. Çekme numuneleri ASTM standartlarına göre çekme deneyi yapılarak, çekme dayanımına ait değerler ile veri seti oluşturulmuştur. Malzeme üretim sürecinde toplanan sıcaklık, hız, katman özelliklerine dair veri seti kullanılarak üretilen malzemenin çekme dayanımı değerleri üç farklı makine öğrenmesi modeli kullanılarak tahmin edilmiştir. Sonuçlar, makine öğrenmesi algoritmaları kullanılarak çekme dayanımını Kısmi En Küçük Kareler algoritması ile %98,3 doğrulukla tahminlediğini göstermiştir.

Anahtar Kelimeler

Eklemeli imalat, Makine öğrenmesi, Eriyik yığma modelleme

Kaynakça

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